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Ben Frederickson's Projects

cub icon cub

Cooperative primitives for CUDA C++.

cudf icon cudf

cuDF - GPU DataFrame Library

cugraph icon cugraph

cuGraph - RAPIDS Graph Analytics Library

cuml icon cuml

cuML - RAPIDS Machine Learning Library

cuvs icon cuvs

cuVS - RAPIDS vector search library

faiss icon faiss

A library for efficient similarity search and clustering of dense vectors.

fmin icon fmin

Unconstrained function minimization in Javascript

gpu-topk icon gpu-topk

Efficient Top-K implementation on the GPU

hugectr icon hugectr

HugeCTR is a high efficiency GPU framework designed for Click-Through-Rate (CTR) estimating training

implicit icon implicit

Fast Python Collaborative Filtering for Implicit Feedback Datasets

mds.js icon mds.js

Classic Multidimensional scaling code in Javascript

merlin icon merlin

NVIDIA Merlin is an open source library providing end-to-end GPU-accelerated recommender systems, from feature engineering and preprocessing to training deep learning models and running inference in production.

nmsbind icon nmsbind

nmsbind: pybind11 bindings for the Non-Metric Space Library (NMSLIB)

nmslib icon nmslib

Non-Metric Space Library (NMSLIB): A similarity search library and a toolkit for evaluation of k-NN methods for generic non-metric spaces.

nvtabular icon nvtabular

A library that sits on top of RAPIDS cuDF library providing a range of benefits for processing extremely large tabular datasets, particularly those that don't fit in GPU or CPU memory. The highlights of NVTabular’s capabilities are fast terabyte-scale data preparation and accelerated tabular data loading, all on GPU, which streamline the first step for both training and inference to any deep recommender system pipelines.

parsesetup icon parsesetup

Parses information from setup.py files, without trusting or installing the package

py-spy icon py-spy

Sampling profiler for Python programs

raft icon raft

RAFT contains fundamental widely-used algorithms and primitives for data science, graph and machine learning.

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